Vectors in Action: Hands-on with FAISS & ChromaDB, Comparative Insights Across Vector DBs

In the era of Generative AI, vector embeddings are the foundation of semantic understanding, powering applications from Retrieval-Augmented Generation (RAG) to recommendation systems. This session offers a deep dive into FAISS and ChromaDB, two leading vector databases enabling high-performance similarity search on massive datasets.

Join us for an interactive and practical session where we’ll not only explore the architectures and use cases of FAISS and ChromaDB but also walk through live hands-on demos, compare them against Pinecone, Weaviate, Qdrant, and Milvus, and discuss best-fit scenarios for different workloads. Whether you are building LLM pipelines, semantic search engines, or intelligent retrieval systems, this session will equip you with the insights and tools you need to succeed.

Key Takeaways:

• Understand the role of vector embeddings in modern AI pipelines.
• Learn the core differences between FAISS and ChromaDB with practical demonstrations.
• Compare top vector databases: FAISS, ChromaDB, Pinecone, Weaviate, Qdrant, and Milvus
• Get hands-on exposure to similarity search, indexing strategies, and performance metrics
• Discover how to integrate vector DBs into RAG pipelines and GenAI applications
• Access open-source tools and code to build your own vector search applications

Prerequisites :

• Basic knowledge of Python programming
• Familiarity with embeddings and machine learning concepts
• Prior exposure to NLP or vector representations (recommended, but not mandatory)

Lakshmi Devi Prakash is an accomplished Senior Data Scientist based in Bengaluru, with over a decade of experience in Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI. She holds a B.E. from SSIT, an M.Tech from MSRIT via GATE, and a Postgraduate specialization in AI/ML from NIT Warangal. Currently, she is pursuing a Ph.D. in Generative AI.

Lakshmi has earned over 140 certifications, including the Azure Data Scientist and AWS Machine Learning Specialist, and holds a patent in multimodal AI systems. Her work has been featured in seven international journals, and she is widely recognized for delivering impactful, data-driven AI solutions across domains.

Lakshmi is deeply passionate about democratizing AI and sharing knowledge to shape the next generation of intelligent systems.